linearfit can probably get you into the ballpark in terms of color, but in terms of the math it does the same thing that color calibration does, albeit a little more blindly. IMO there's no requirement to do LinearFit on RGB images if you are going to do color calibration - in essence the CC step is just going to undo whatever LF did.
here is where i think LF is applicable:
1) when doing RGB combines of narrowband data. since color calibration is 'meaningless' with SHO blends, CC may not be applicable (i say may, because people do use PhotometricColorCalibration "off label" with SHO data and it apparently works, but the results are physically meaningless of course. and one way to balance the channels in an SHO image is to use ColorCalibration over the entire image, but this is sort of equivalent to using LinearFit...)
2) when doing DBE on RGB data where the channels have wildly different brightness (which is pretty common of course). because DBE has only 1 thresholding control, in order to pick up pixels in the brightest channel you may need to set the threshold control so high that the other two channels are oversampled. in this case it helps to have the channels aligned in brightness before you start with DBE.
3) when making mosaics, to get the brightness of each pane to similar levels before assembling the panes. however, LF needs to be run on registered images, which of course mosaic panes are not. you need to run LF on the intersection of the panes, which was a hassle until David Ault wrote a script called DNALinearFit which does the intersection calculation automatically.
as far as your question about which image to pick... just pick one channel as the reference and apply LF to the other two channel images. it's all relative so to speak so it does not matter which image you choose as the reference as long as you use it on both of the other two images. not to confuse the point, but in fact if you chose G as your reference and applied it to B, then chose B as the reference and applied it to R, the result would probably be much the same as using G as the reference for B and R since after linear fitting B, it's statistics should be comparable to G.
rob