I'm afraid, this is not correct. Quite counterintuitively, "normalization" means subtracting the mean and dividing by the standard deviation. What you are referring to is non-linear transformations.

I do not know under which specifications you run SEM. But even if you choose the option assuming normal distributions of the relevant variables (which you do not have to), the situation is the following. If the sample size is very large relative to the number of parameters, you do not have to apply non-linear transformations to make the variables normal. The SEM tests are still valid... If the sample size is not very large relative to the number of parameters, you should look into the residuals of the linear/non-linear models which are links in the SEM map. If the residuals exhibit non-normality, you have to transform. You can use parametric transformations (Box-Cox for example), semi-parametric transformations (kernel smoothing) or non-parametric transformations (empirical distribution function). The parametric approach is easiest.