You have to realise what the transformations are doing.

is compressing the y values relative to the smaller data values

is compressing large y values relative to the smaller data values, to a greater extent than

(values of y less than 1, become greater than 1, great becomes less etc.)

Spreads out the high x values relative to the smaller values.
I have the circle of transformations (essentials p190) although it doesn't really specify which ones are more correct for different situations.
The only way of figuring this out, I guess, is too see which transformation gives the
best linear model.
It does not matter if you have to "guess", in essence, the points on the scatterplot because each transformations Coefficient of Determination is going to be different. So if you punch in your estimates on your calculator and note down all

values for each transformation, you'll get your answer. It is tedious, but there isn't a way around it (to my knowledge). You could also create a residual plot in order to evaluate the linearity. Realistically, it won't take you more than a couple of minutes to do this, and it's the best way to get an accurate answer.