A simple word2vec model is not capable of such task, as it only relates word semantics to each other, not the semantics of whole sentences. Inherently, such a model has no generative function, it only serves as a look-up table.
Word2vec models map word strings to vectors in the embedding space. To find similar words for a given sample word, one can simply go through all vectors in the vocabulary and find the ones that are closest (in terms of the 2-norm) from the sample word vector. For further information you could go here or here.
However, this does not work for sentences as it would require a whole vocabulary of sentences of which to pick similar ones - which is not feasible.
Edit: This seems to be a duplicate of this question.