How to estimate octave and size for visual features positioned at Harris corners
I'm not sure there is actually a good response to your precise question : the scale-space thing of SIFT and SURF was actually developed to automatically estimate the "good" relevant neighborhood size around a corner-like keypoint (which is what good features to track are).

Now, more positive answers would be:


build a database of keypoints and good matches (e.g. using square calibration patterns) and create an automated performance assessment on this database to find the correct size. This task can actually really be automated (see the work by Mikolajczyk and Schmid about point matching evaluation)
embed your good features in an image pyramid to also have some kind of scale associated with them. You can look for references on multi-scale FAST and Harris interest points, that do something very similar to this procedure.


To heuristically find the maximum block size, you can compute estimates of your images with a box blur (which is more or less what the blockSize operator does) and see when the corner disappears. Note however that more blur takes the corner away from its true location. 

If you're really looking for some quick-and-dirty fix, try sizes between 5x5 and 11x11 (typical sizes used in stereo block matching). If you're looking for an intellectually satisfying criterion, then try to maximize the likelihood of good matching of two feature points under your noise level.