If it is also true that hypotheses which are easier to locate make more predictions

This doesn't sound right to me. Imagine you're tossing a coin repeatedly. Hypothesis 1 says the coin is fair. Hypothesis 2 says the coin repeats the sequence HTTTHHTHTHTTTT over and over in a loop. The second hypothesis is harder to locate, but makes a stronger prediction.

The proper formalization for your concept of locate-ability is the Solomonoff prior. Unfortunately we can't do inference based on it because it's uncomputable.

Maxent and friends aren't motivated by a desire to formalize locate-ability. Maxent is the "most uniform" distribution on a space of hypotheses; the "Jeffreys rule" is a means of constructing priors that are invariant under reparameterizations of the space of hypotheses; "matching priors" give you frequentist coverage guarantees, and so on.

Please don't take my words for gospel just because I sound knowledgeable! At this point I recommend you to actually study the math and come to your own conclusions. Maybe contact user Cyan, he's a professional statistician who inspired me to learn this stuff. IMO, discussing Bayesianism as some kind of philosophical system without digging into the math is counterproductive, though people around here do that a lot.