Genetic Programming is a branch of Evolutionary Computation devoted to the evolution of programs. Several genetic operators have been proposed to increase the power of the search, given that the space of admissible programs is very challenging to be properly explored toward high quality solutions. Semantically-driven genetic operators are gaining more attention lately, given that the behaviour of the search operators are more predictable, possibly leading to a more efficient evolution. Nonetheless, Semantic Genetic Programming may undergo the bloat phenomenon, characterized by an uncontrolled increase in the program size along the generations. Some attempts have been made in the literature to refrain code bloat, and here we are proposing three novel semantic-driven mutation operators for the tree structure codification. A multi-objective perspective is adopted, where the mutated subtrees correspond to nondominated instances in a previously defined library of candidate subtrees. Several conflicting objectives may be incorporated into the decision making process, such as accuracy, semantic distance to a reference behaviour, and size of the subtree. Experimental results reveal that our proposed operators are effective in restraining bloating, without a negative impact on the other performance metrics, and are competitive with other relevant approaches available in the literature.