The statistics that are typically calculated along with linear regression assume that residuals are normally distributed. It's not necessarily a problem that your response variable is non-normal - what matters are the residuals. I would go ahead and fit the model you have in mind and test the residuals.
If you still think you require a transformation, then I would do it, and try to use effects plots plotted on the original scale to help your readers understand the relationship between the predictors and the response.
One thing I notice is that your response variable is perhaps on a discrete scale. This might push you to treat it as an ordinal categorical variable and do your regression with polr.